Skill UI

This guide provides a comprehensive technical deep dive for developers migrating image generation workflows from services like DALL-E, Midjourney, and Stable Diffusion to Ideogram. It covers API parameter mapping, architectural patterns (like the Adapter pattern), and provides code examples to ensure a stable, gradual, and feature-rich transition to Ideogram's platform.

A comprehensive guide to implementing robust observability for Ideogram AI image generation APIs. Learn how to track critical metrics such as generation duration (latency), estimated API costs, error rates, and content safety rejections. This skill involves instrumenting API calls using TypeScript, integrating cost estimation, and setting up Prometheus alerting rules to ensure stable and reliable AI operation.

This skill guide provides advanced techniques for optimizing Ideogram API usage. It covers selecting optimal model tiers (e.g., Turbo vs. Standard) based on desired quality and speed, implementing prompt-based caching to reduce API calls and costs, and managing parallel generation with concurrency limits for high throughput. Additionally, it demonstrates CDN integration for rapid asset delivery.

A comprehensive checklist and guide for deploying and maintaining Ideogram image generation services in a live production environment. It covers essential steps including API key management, request handling best practices, ensuring image persistence in durable storage, comprehensive error handling (401, 422, 429), and defining robust rollback procedures.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.

This skill guides developers on building scalable, event-driven pipelines around Ideogram's synchronous API. It demonstrates implementing robust async patterns, including using job queues (like BullMQ) for batch generation, handling webhook callbacks for post-processing, and setting up full asset modification pipelines. Ideal for production-grade marketing and content workflows.

This skill leverages Google Gemini's advanced image generation model to create high-quality, context-specific visuals. It is essential for developers needing placeholder images for UIs, generating detailed diagrams for technical documentation, or visualizing abstract concepts, logos, and assets across cross-platform projects (Windows, macOS, Linux).

Provides automated guidance and code generation for Jest-based test automation, covering unit, integration, mocking, and configuration patterns to ensure production-ready suites.

Automated guidance for jwt token validator tasks within the Security Fundamentals domain, offering step-by-step help, industry best practices, and production-ready code/configuration generation when you mention jwt token validator or seek secure authentication patterns.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

This tool provides comprehensive Python solutions for processing batches of text-to-video requests using the Kling AI API. It handles controlled parallelism, rate-limiting, and detailed status tracking. Use this when building robust content pipelines that require generating multiple videos efficiently from a list of prompts, supporting synchronous, asynchronous, and webhook callback methods.

This guide provides a comprehensive reference for diagnosing and resolving common errors encountered when interacting with the Kling AI API. It covers various HTTP status codes (e.g., 400, 401, 429), task-level failures (e.g., content policy violations, low image quality), proper JWT token management, and implementing rate limit handling with exponential backoff. Use this resource when troubleshooting failed video generation or API integration issues.